Genetic mechanisms regulating gene expression encompass complex processes such as transcription, translation, epigenetic modifications, and interactions of regulatory elements. These mechanisms play a crucial role in shaping phenotypic diversity in humans. High-throughput technologies, such as expression microarrays and next-generation sequencing (NGS), have enabled precise analysis of transcripts for thousands of genes genome-wide. These methods have enabled researchers to measure gene expression levels in various tissues and cells and to gain deeper insights into previously inaccessible biological processes. Numerous studies show that gene expression varies significantly among individuals. However, there are also notable differences between populations from different continental groups, driven by genetic, epigenetic, environmental factors, and natural selection. Furthermore, disease states represent an important factor influencing gene activity, as they can significantly alter the transcriptomic profiles of individual cells. In this context, comparative population genetic studies help uncover the molecular mechanisms underlying complex phenotypic traits and identify population-specific features of transcriptomic profiles in both health and disease. However, despite significant progress in this field, many aspects remain underexplored. Specifically, the distribution of gene expression variability among populations, the degree of research coverage for specific ethnic groups, the spectrum of biological materials used, and the contribution of population affiliation to observed differences in gene expression during pathological conditions require further investigation. This review presents an overview of contemporary research focused on analyzing variability in expression profiles across different human populations. It summarizes findings from individual studies, outlines the advantages and limitations of the methods employed, highlights key research directions in population transcriptomics, and discusses potential practical applications of the data obtained.
Babovskaya et al. (Thu,) studied this question.